Short-term Traffic Flow Prediction Based on Multivariable Phase Space Reconstruction and LSSVM

Abstract

Real-time and accurate short-term traffic flow prediction is the premise and key of intelligent traffic control and guidance system. According to this problem, this paper put forward a prediction model based on multivariable phase space reconstruction and least squares support vector machine (LSSVM). First, the model confirms embedding dimension and delay time of the traffic flow, occupancy and average speed time series by analyzing their chaotic characteristics, and reconstructs multivariable state space. Second, the phase points obtained after reconstruction are as input, and the last traffic flow parameters came from the following phase points are as output. Finally, the LSSVM which be trained is adapted to realize short-term traffic flow prediction. This research compares this model with a model based on univariate phase space reconstruction and LSSVM, and the results show that the model proposed in this paper predicts better.